In the oil and gas industry, seismic prospecting techniques commonly are used to aid in the search for and evaluation of subterranean hydrocarbon deposits. A seismic prospecting operation typically proceeds in three separate stages: data acquisition, data processing, and data interpretation. Success of the prospecting operation often depends on satisfactory completion of all three stages.
In the data acquisition stage, a seismic source emits an acoustic impulse known as a seismic signal that propagates into the earth and is at least partially reflected by subsurface seismic reflectors (i.e., interfaces between underground formations having different acoustic impedances). The reflected signals (known as seismic reflections) are detected and recorded by an array of seismic receivers located at or near the surface of the earth, in an overlying body of water, or at known depths in boreholes.
During the data processing stage, raw seismic data recorded in the data acquisition stage are refined and enhanced using a variety of procedures that depend on the nature of the geologic structure being investigated and on characteristics of the raw data. In general, the purpose of the data processing stage is to produce an image of the subsurface from the recorded seismic data for use during the data interpretation stage. The image is developed using theoretical and empirical models of the manner in which the seismic signals are transmitted into the earth, attenuated by subsurface strata, and reflected from geologic structures.
The purpose of the data interpretation stage is to determine information about the subsurface geology of the earth from the processed seismic data. The results of the data interpretation stage may be used to determine the general geologic structure of a subsurface region, to locate potential hydrocarbon reservoirs, to guide the development of an already discovered reservoir, or to help manage hydrocarbon extraction operations.
Often, three-dimensional seismic data are a useful tool for seismic prospecting operations. As used herein, a three-dimensional seismic data volume is a three-dimensional volume of discrete x-y-z or x-y-t data points, where x and y are mutually orthogonal, horizontal directions, z is the vertical direction, and t is two-way vertical seismic signal travel time. In subsurface models, these discrete data points are often represented by a set of contiguous hexahedrons known as samples, cells or voxels, with each voxel representing a volume surrounding a single data point. Each data point, cell, or voxel in a three-dimensional seismic data volume typically has an assigned value (data sample) of a specific seismic data attribute such as seismic amplitude, acoustic impedance, or any other seismic data attribute that can be defined on a point-by-point basis. One column of such a volume is often called a seismic data trace or simply a trace, while a slice through such a volume is often called cross section, or simply section.
A common issue in three-dimensional seismic data interpretation concerns extraction of geologic features from a three-dimensional seismic data volume, evaluation of their geometric relationships to each other, and implications for connectivity. A seismic object, geobody or simply body generally is a region of connected voxels in a three-dimensional seismic data volume in which the value of a certain selected seismic attribute (acoustic impedance, for example) satisfies some arbitrary threshold requirement. For example, the number may be greater than some minimum value and/or less than some maximum value. Bulk processing of a seismic data volume at a certain attribute threshold results in the detection of one or more seismic geobodies. The geobodies may correspond to actual underground reservoirs. Seismic data interpretation time can be reduced significantly via bulk processing a seismic data volume, and generating a collection of geobodies. This processing, of course, is carried out using a suitably programmed computer.
One technique for identifying and extracting geobodies from a three-dimensional seismic data volume is known as seed picking (also known as region growing). Seed picking results in a set of voxels in a three-dimensional seismic data volume that fulfill user-specified attribute criteria and are connected. Seed picking is typically an interactive method, where the user specifies the initial seed voxel and attributes criteria. The seed picking algorithm marks an initial voxel as belonging to the current object, and tries to find neighbors of the initial voxel that satisfy the specified attribute criteria. The new voxels are added to the current object, and the procedure continues until it is not possible to find any new neighbors fulfilling the specified criteria.
Another technique for identifying and extracting geobodies from a three-dimensional seismic data volume is known as thresholding. Thresholding results in multiple sets of voxels in a three-dimensional seismic data volume. Each set of voxels fulfills user-specified attribute criteria and is connected. Thresholding is an automated method, where the user specifies the attribute criteria. The thresholding algorithm examines every voxel with regard to the specified attribute criteria. Acceptable voxel are grouped into contiguous objects based on a user-specified connectivity criterion. Each so isolated object is typically given a unique identifier.
Seed picking and thresholding typically involve assigning a criterion for connectivity. There are three criteria commonly used, although others may be defined and used. One definition is that two cells or voxels are connected (i.e., are neighbors) if they share a common face. By this definition of connectivity, a cell (or voxel) can have up to six neighbors. Another criterion for being a neighbor is sharing either an edge or a face. By this criterion, a cell (or voxel) can have up to eighteen neighbors. The last common criterion for being a neighbor is sharing either an edge, a face, or a corner. By this criterion, a cell (or voxel) can have up to twenty-six neighbors.
As described in U.S. Pat. No. 5,586,082 to Anderson, et al., one exemplary method of seed picking or seed growing involves determining how geobodies that are distinct at one threshold of a chosen attribute may be connected at another threshold. For example, high amplitude regions, suggestive of petroleum presence, may be identified using seismic attribute analysis, with the object of determining oil or gas migration pathways connecting those regions, or alternatively to determine that certain regions are unconnected.
Methods such as disclosed in U.S. Patent Application Publication 2012/0234554 by Kumaran pursue a different strategy to form geobodies. Instead of forming geobodies by inspection of single attribute values, geobodies are formed by way of a texture analysis, i.e., an analysis of the distribution of attribute values within specified neighborhoods. Geobodies are then formed by similarity in texture.
Other methods such as that disclosed in PCT Application Publication WO 2009/126951 employ a seed point and a seed surface surrounding the seed point to create a second surface guided by the seismic attribute and some measure of surface complexity.
Commercial seed detection methods are often solely cell connectivity-based and may lack adequate provisions for analysis of the resulting geobodies. U.S. Pat. No. 6,823,266 to Marek Czernuszenko et al. and U.S. Pat. No. 6,674,689 to Paul Dunn and Marek Czernuszenko describe methods for analyzing the connectivity and three-dimensional characteristics (shape) of geobodies extracted from three-dimensional seismic volumes. These methods allow the geoscientist to collect many geobodies into meaningful assemblages. One notable use of this assemblage of geobodies is in a reservoir characterization and modeling workflow as described in U.S. Pat. No. 7,925,481 to van Wagoner et al.
Since geobody detection and analysis is often a preliminary step in a seismic-based reservoir modeling workflow, considerable effort has been devoted to refining the process. Any inaccuracies in the initial geobody definition can propagate errors in a cascade down through the rest of the workflow.
All seed detection algorithms can produce stratigraphically unreasonable geobodies. The generation of unreasonable geobodies is a result of a fundamental dilemma in volumetric seed detection using attribute thresholds. Narrow (i.e. high) thresholds tend to yield many simple, isolated bodies that may be readily interpreted in terms of the stratigraphic features they represent (e.g. channel fills, delta or deep sea fan lobes), but are difficult to assemble to larger, yet stratigraphically reasonable assemblages. Wide (i.e. low) thresholds, conversely, result in selection of large numbers of voxels that form complex, amorphous geobodies that are not stratigraphically reasonable and are difficult to interpret.
U.S. Pat. No. 7,024,021 to Paul Dunn and Marek Czernuszenko describes a method (called StrataSeed) for including connectivity criteria beyond cell-to-cell contact through the integration of criteria that include larger scale features such as reflections or other layered structures composed of many individual cells. The method reduces the picking of unreasonable bodies and addresses the separation of amorphous, complex geobodies into simpler components through a map view criteria check during geobody detection. A disadvantage of the method of U.S. Pat. No. 7,024,021 is that the boundaries between individual components of larger assemblages or geobodies are determined by the order in which the voxels are selected during bulk processing of the seismic cube.
U.S. Patent Application Publication No. 2010/0274543 to Walker et al. discloses a rule-based method for identifying and extracting connected bodies using wide thresholds of seismic attributes while still yielding stratigraphically reasonable individual. The method works by assigning overlapping portions of the complex geobody to separate components, or by systematically removing voxels according to specified rules until the geobody falls apart into smaller components. The disadvantage of the former method is that the cuts are not necessarily made where expected, while the later method riddles the geobody with holes and creates components with frazzled edges because a great number of voxels may need to be removed before the geobody separates. Moreover, decomposition is only initiated when overlap exists.
What is needed is a method that allows decomposition of any geobody formed from contiguous voxels and obtained by any method into simple, compact components. Preferably, this method can be employed interactively or automatically. The present inventive method satisfies at least these needs.